Articles | Volume 18, issue 18
https://doi.org/10.5194/bg-18-5185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-5185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2021
Research article |
| 22 Sep 2021
| 22 Sep 2021
Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework
Juhwan Lee
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Raphael A. Viscarra Rossel
CORRESPONDING AUTHOR
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Mingxi Zhang
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Zhongkui Luo
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, China
Ying-Ping Wang
CSIRO Oceans and Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia
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Charlotte Decock, Juhwan Lee, Matti Barthel, Elizabeth Verhoeven, Franz Conen, and Johan Six
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-221, https://doi.org/10.5194/bg-2022-221, 2022
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One of the least well understood processes in the nitrogen (N) cycle is the loss of nitrogen gas (N2), referred to as total denitrification. This is mainly due to the difficulty of quantifying total denitrification in situ. In this study, we developed and tested a novel modeling approach to estimate total denitrification over the depth profile, based on concentrations and isotope values of N2O. Our method will help close N budgets and identify management strategies that reduce N pollution.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
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This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
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We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
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Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced mechanistic SOC models struggle to do this task. Here we apply multiple explainable machine learning methods to identify missing variables and misrepresented relationships between environmental factors and SOC in these models, offering new insights to guide model development for more reliable SOC predictions.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
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Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Yang Hu, Adam Cross, Zefang Shen, Johan Bouma, and Raphael A. Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3939, https://doi.org/10.5194/egusphere-2024-3939, 2025
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We reviewed the literature on soil health definition, indicators and assessment frameworks, highlighting sensing technologies' significant potential to improve current time-consuming and costly assessment methods. We proposed a soil health assessment framework from an ecological perspective free from human bias, that leverages proximal sensing, remote sensing, machine learning, and sensor data fusion to enable objective, rapid, cost-effective, scalable, and integrative assessments.
Thorsten Behrens, Karsten Schmidt, Felix Stumpf, Simon Tutsch, Marie Hertzog, Urs Grob, Armin Keller, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2810, https://doi.org/10.5194/egusphere-2024-2810, 2024
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We integrate various methods to create soil property maps for soil surveyors, which they can utilize as a reference before beginning their fieldwork. A new sampling design based on a geographical stratification is proposed focussing on local feature space variability. It allows for a systematic analysis of predictive accuracy for varying densities. The spectral and spatial models yielded high accuracies. Our study highlights the value of integrating pedometric technologies in soil surveys.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
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Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Lewis Walden, Farid Sepanta, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2023-2464, https://doi.org/10.5194/egusphere-2023-2464, 2023
Preprint archived
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We characterised the chemical and mineral composition of soil organic carbon fractions with mid-infrared spectroscopy. We identified unique and shared features of the spectra of carbon fractions, and the interactions between their organic and mineral components. These interactions are key to the persistence of C in soils, and we propose that mid-infrared spectroscopy could help to infer stability of soil C.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
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We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Charlotte Decock, Juhwan Lee, Matti Barthel, Elizabeth Verhoeven, Franz Conen, and Johan Six
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-221, https://doi.org/10.5194/bg-2022-221, 2022
Preprint withdrawn
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One of the least well understood processes in the nitrogen (N) cycle is the loss of nitrogen gas (N2), referred to as total denitrification. This is mainly due to the difficulty of quantifying total denitrification in situ. In this study, we developed and tested a novel modeling approach to estimate total denitrification over the depth profile, based on concentrations and isotope values of N2O. Our method will help close N budgets and identify management strategies that reduce N pollution.
Zefang Shen, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, Yang Hu, and Raphael A. Viscarra Rossel
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We compared miniaturised visible and near-infrared spectrometers to a portable visible–near-infrared instrument, which is more expensive. Statistical and machine learning algorithms were used to model 29 key soil health indicators. Accuracy of the miniaturised spectrometers was comparable to the portable system. Soil spectroscopy with these tiny sensors is cost-effective and could diagnose soil health, help monitor soil rehabilitation, and deliver positive environmental and economic outcomes.
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
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We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
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Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
Short summary
Short summary
This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
Short summary
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Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
Short summary
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Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
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Short summary
We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
We performed Roth C simulations across Australia and assessed the response of soil carbon to...
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